Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length.
| Published in | Advances (Volume 3, Issue 3) |
| DOI | 10.11648/j.advances.20220303.22 |
| Page(s) | 125-131 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Path Planning, Genetic Algorithm, GA, Mobile Path
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APA Style
Faten Abushmmala, Iyad Abuhadrous. (2022). Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances, 3(3), 125-131. https://doi.org/10.11648/j.advances.20220303.22
ACS Style
Faten Abushmmala; Iyad Abuhadrous. Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating. Advances. 2022, 3(3), 125-131. doi: 10.11648/j.advances.20220303.22
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Download@article{10.11648/j.advances.20220303.22,
author = {Faten Abushmmala and Iyad Abuhadrous},
title = {Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating},
journal = {Advances},
volume = {3},
number = {3},
pages = {125-131},
doi = {10.11648/j.advances.20220303.22},
url = {https://doi.org/10.11648/j.advances.20220303.22},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.advances.20220303.22},
abstract = {Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length.},
year = {2022}
}
TY - JOUR T1 - Adaptable Genetic Algorithm Path Planning of Mobile Robots Based on Gene Reallocating AU - Faten Abushmmala AU - Iyad Abuhadrous Y1 - 2022/09/29 PY - 2022 N1 - https://doi.org/10.11648/j.advances.20220303.22 DO - 10.11648/j.advances.20220303.22 T2 - Advances JF - Advances JO - Advances SP - 125 EP - 131 PB - Science Publishing Group SN - 2994-7200 UR - https://doi.org/10.11648/j.advances.20220303.22 AB - Mobile path planning is rich field of employing artificial intelligence and machine learning algorithms to obtain the most effective outcomes. The Path planning task is a problem. The goal of path design is to find the quickest and most obstacle-free route from a starting point to a destination state. A set of states (position and orientation) or waypoints can make up the path. A map of the surroundings, as well as the start and target states, are needed for path planning. Path planning applications are diverse and unlimited, such as Automated robot navigation, autonomous vehicle Robotic surgery, digital animation of characters, and others. Different algorithms provide different solutions to this problem; usually the metric used to evaluate certain path effectiveness doesn’t take into consideration the physical attributes of the mobile robot. In this paper, an attempt is made to find the best path in terms of distance and smoothness (minim number of rotations); the smoothness means decreasing power consumption since the rotations take a lot of power to be executed. A traditional genetic algorithm is used to find the best path, and then modification is used to improve the path's characteristics. The experimental results obtained using MATLAB Simulator indicate that the enhanced approach applied in the genetic algorithm provides much better outcomes, the path edges are minimized along with the path length. VL - 3 IS - 3 ER -
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